©2008 metrum research group LLC
PK-PD Modeling and DosageDetermination for Proof-of-Concept Trials
Marc R. Gastonguay, Ph.D.([email protected])
IMMPACT-VIII
Early Clinical Study Designs, Emphasizing Proof-of-Concept Trials
June 12-14, 2008
Arlington, VA
©2008 metrum research group LLC 2
PKPD in Proof of Concept Trials: IMMPACT 2008
Overview
- PK/PD (Exposure-Response) and Model-Based DrugDevelopment
- Role of Exposure-Response Modeling in Proof-of-ConceptTrials Planning and Design
Analysis and Quantitative Support for PoC Determination
Building Knowledge for Later Stage Development
Other examples of E-R utility
- Summary Points
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PKPD in Proof of Concept Trials: IMMPACT 2008
Innovation: Planes are modeled long before takeoffNASA Aerospace Engineering Grid
•Lift Capabilities•Drag Capabilities•Responsiveness
•Deflection capabilities•Responsiveness
•Thrust performance•Reverse Thrust performance•Responsiveness•Fuel Consumption
•Braking performance•Steering capabilities•Traction•Dampening capabilities
Crew Capabilities- accuracy- perception- stamina- re-action times- SOP’s
Engine Models
Airframe Models
Wing Models
Landing Gear Models
Stabilizer Models
Human Models
Whole system simulations are produced by couplingall of the sub -system simulations
It takes a distributed virtual organization to design,simulate and build a complex system like an aircraft
http://grids.ucs.indiana.edu/ptliupages/presentations/cendiapril25-05.ppt
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PKPD in Proof of Concept Trials: IMMPACT 2008
Pharmacometrics…the science ofinterpreting and describingpharmacology in a quantitative fashion(e.g. through modeling and simulation)
DOSE CONCENTRATION RESPONSE
PK PD
DIS
EA
SE
PR
OG
RE
SS
ION
TRIAL DESIGNS &DEVELOPMENT STRATEGY KNOWLEDGE
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PKPD in Proof of Concept Trials: IMMPACT 2008
Modeling and Simulation: A Tool to Facilitatethe Learn-Confirm Continuum
Collect data
Build models to describe data andconfirm prior knowledge
Use M&S to learn from new data andexplore future outcomes
Make informed decisions
Perform new experiment (study)
Sheiner LB. Learning versus confirming in clinical drug development.Clin Pharmacol Ther 1997; 61(3):275-91.
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PKPD in Proof of Concept Trials: IMMPACT 2008
M&S Throughout Drug Development
Simulation & Optimizationof Phase III Trial Designs
Simulation-Guided(Adaptive) Phase IIDesigns: EarlyProbability of Success &Dose-Response
Pop PK, E-R forLabeling andConfirmatoryEfficacy Support
Preclinical &Early Development:PK-PD, SystemsBiology M&S
Therapeutic AreaKnowledge: DiseaseProgression &Target ResponseProfile
ToxicologyPKBiomarkerE-R
HumanMTD, PKBiomarker,Tolerability, E-R
Biomarker, Efficacy,Tolerability E-RDose-ResponseCovariates, Pop PK-PD
Efficacy,Safety & DoseSpecialPopulations
NewFormulationsBridge to NewIndications
Proof of Concept:Probability-BasedDecision Rule
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What to Learn?
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NG2-73 AUC (ng*hr/mL)EXPOSURE (e.g. AUC, Cmax, Css avg)
RES
PON
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Tole
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PKPD in Proof of Concept Trials: IMMPACT 2008
- Stanski.Model-BasedDrugDevelopment:ACriticalPathOpportunity,March18,2004
Regulatory Support for M&S
http://www.fda.gov/oc/initiatives/criticalpath/presentations.html
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PKPD in Proof of Concept Trials: IMMPACT 2008
Regulatory Support for M&S:Guidance Documents
- Population Pharmacokinetics (FDA and EMEA)- Exposure-Response Relationships (FDA)- Dose-Response Information to Support Drug
Registration (ICH-E4)- General Considerations for the Clinical Evaluation of Drugs (FDA 77-
3040)- General Considerations for Pediatric Pharmacokinetic Studies (FDA)- Pharmacokinetics in Patients with Impaired Renal Function (FDA)- Pharmacokinetics in Patients With Impaired Hepatic Function (FDA)- Studies in Support of Special Populations:
Geriatrics (ICH-E7)- Ethnic Factors in the Acceptability of Foreign
Clinical Data (ICH-E5)- Clinical Investigation of Medicinal Products in the Pediatric Population
(ICH-E11)
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PKPD in Proof of Concept Trials: IMMPACT 2008
Determination of PoC
- Primary Challenge: Define decision criteria for PoCdetermination Proof of mechanism
Statistically significant efficacy response with approval endpoint
Acceptable probability of achieving multivariate target responseprofile
Comparability to active control
- Once defined, probability of meeting PoC decision criteriafor different trial designs can be explored throughmodeling and simulation
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Target Response Profile
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hrs
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Dose
(m
g)
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NG2-73 AUC (ng*hr/mL)EXPOSURE (e.g. AUC, Cmax, Css avg)
RES
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PKPD in Proof of Concept Trials: IMMPACT 2008
PK and Exposure-Response M&SOpportunities in PoC
- PK Modeling Understand PK in target population and possibly reduce inter-
individual variability in exposure to increase signal/noise: dosingindividualization
Select PoC doses with minimal exposure overlap Explain unexpected outcomes (e.g. unknown phenotypic
differences in PK) Adjust for formulation differences
- E-R Modeling Assessment of E-R relationships for multiple endpoints (e.g. after
dose-ranging based on efficacy endpoint) Basis for trial simulations: explore performance/options in silico
before initiating clinical trial
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PKPD in Proof of Concept Trials: IMMPACT 2008
Impact of E-R Varies with PoC Trial DesignsMTD-Type PoC Design
Typically 1 active treatment dose vs. referencetreatment
Dose selected based on Phase I MTD Standard pair-wise statistical comparison
Dose-Ranging PoC Design Multiple doses investigated Dose-range informed by preclinical data, Phase
I, biomarker, competitor data Model-based data analysis Often multi-variable PoC assessment
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PKPD in Proof of Concept Trials: IMMPACT 2008
Exposure-Response in MTD-Type PoC:Proceed with Caution
- Single active treatment arm atMTD (300 mg) vs. Placebo
- Obtain PK in all individuals
- Explore resulting relationshipbetween exposure (Cavg) andResponse (1 observation perindividual)
- Can we make an accurateassessment of the PK-PDrelationship from this design?
Problem described in: Nedelman JR, Rubin DB, Sheiner LB. Diagnostics for confounding inPK/PD models for oxcarbazepine. Stat Med. 2007 Jan 30;26(2):290-308.
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Exposure-Response in MTD-Type PoC- Consider possible inter-individual correlation between PKand PD
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Exposure-Response in MTD-Type PoC
- Resulting exposure-response relationships are misleading
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PKPD in Proof of Concept Trials: IMMPACT 2008
Exposure-Response in MTD-Type PoC- One solution: Obtain within-individual E-R (e.g. crossover)analyzed with mixed-effects modeling
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Exposure-Response in MTD-Type PoC- Another solution: Population E-R with broad dose-range
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PKPD in Proof of Concept Trials: IMMPACT 2008
PK-PD in Planning and Design of PoC Trials
- Use prior information, when available
Phase I PK, tolerability, biomarkers
Pre-clinical estimates of effective concentrations,relative potency
Competitor data
Therapeutic area knowledge
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Toxicity E-R to Inform PoC Dose Selection
Data from SAD, MADin healthy volunteers
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Probability of QTc Prolongation
• Explore probability of QTc – related toxicity atvarious doses from Phase I data
• Project QTc prolongation at expected Cmax, giventop dose and DDI
• Define dose-limit and early probability ofcompound viability
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PKPD in Proof of Concept Trials: IMMPACT 2008
Modeling Biomarker Data: Phase I MD Study- PK-PD relationship evident & quantifiable (‘Emax’ model)
- Establish target PoM
- Set doses for investigation in PoC = concentrations within apparentefficacious range
Concentration
Mar
ker
ConcEC
ConcEE
++=
50
max
0
*Marker
0 1 2 3 4
7
6
5
4
3
2
1
0
O PlaceboO Dose 1O Dose 2O Dose 3--- Model Prediction
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PKPD in Proof of Concept Trials: IMMPACT 2008
E-R Analysis of PoC Trials
- Example Parallel groups: 4 active doses + placebo + active control
(competing therapy)
Multiple Endpoints: biomarker 1 (efficacy), biomarker 2(undesired), clinical outcome 1
PoC determination based on model-based posterior probability ofreaching target response profile
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PKPD in Proof of Concept Trials: IMMPACT 2008
E-R Based PoC: Test & Active ComparatorResponse: Biomarker 1 (efficacy)
Drug X (red),Comparator Y (blue)
Median Concentration
Media
n R
esponse
EC50 = dashed lines
Median observation at each collection time for each treatment (circles)
PK-PD Model Prediction (solid line)
01
23
45
- Drug X (red) was more potent than Comparator Y (blue)- Relative potencies (EC50 of X vs. Y) very consistent across multiple response variables
Plasma Drug Concentration
Bio
mar
ker 1
Res
pons
e
RXij = E0i + EMAXi*Ci/(EC50Xi + Ci) + eij
RYij = E0i + EMAXi*Ci/(EC50Yi + Ci) + eij
Target
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PKPD in Proof of Concept Trials: IMMPACT 2008
- Identified Drug X concentrations associated with BM II effect- Consider doses that provide for target concentrations
PoC: E-R for Biomarker 2 (undesired)
Concentration rangeassociated with “no effect”
Concentration rangeassociated with “effect”
Concentration
Mar
ker I
I
Target
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PKPD in Proof of Concept Trials: IMMPACT 2008
Res
pons
e I
Dos
e
Cmax (concentration)Drug X
E-R for Clinical Outcome I
Target
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PKPD in Proof of Concept Trials: IMMPACT 2008
- Drug X posterior probability distribution for target response meetsPoC criterion, but which doses should go into Phase 2b, whereprimary endpoint will be an approval outcome measure?
- Comparator Y Dose-Response Literature data Model = Nonlinear ‘Emax’ model for mean relationship Uncertainty range: Based on standard errors of parameter estimates
- Scaled for Approximate Dose-Response of Drug X Based on biomarker relative EC50 of Drug X vs. Comparator Y Accounted for PK differences Additional variability for uncertainty in scaling ratios
Building Knowledge for Phase 2b
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Dose Y
Response
Dose-Response Model for Comparator Y: 2b Response
0 1 2 3 4
0
1
2
3
4
Literature data (o)
Mean Prediction (___)
Uncertaintyrange: basedon 95% CI’s ofparameterestimates
Dose
Res
pons
e
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PKPD in Proof of Concept Trials: IMMPACT 2008
- Select doses to further characterize (reduce uncertainty in) response surface
- Target doses ~ 50% (ED50), 80% (ED80) & max effects (Emax)
0
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40 1 2 3 4
Scaled Dose-Response for Drug X:Predicted 2b Response
Dose
Res
pons
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Other Examples of E-R in Analgesic PoC Trials
- Dissociation of rescue drug effects from test treatment
- Model-based inferences with dropout (missing data)
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Dissociating Treatment Effects fromRescue Dose Effects
- Chronic pain PoC design (PBO plus 4 dose levels)
- Acetaminophen rescue (500 mg) allowed as needed
- Reduction in pain intensity is primary endpoint
- Problem: How to interpret pain response in presence of rescue?
- Proposal: Analyze entire data set with model-based analysis using 2simultaneous exposure-response relationships: Study Drug E-R
Rescue E-R
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PKPD in Proof of Concept Trials: IMMPACT 2008
Consideration
- Potential delay between plasma exposure and exposure atsite of action (e.g.,CNS) May be more pronounced with
▶ with acute or ‘prn’ dosing▶ shorter t1/2 and/or rapid Tmax
Figure from: Shinoda S, Aoyama T, Aoyama Y, Tomioka S, MatsumotoY, Ohe Y 2007. Pharmacokinetics/pharmacodynamics of acetaminophenanalgesia in Japanese patients with chronic pain. Biol Pharm Bull30(1):157-161
Also see: Staahl C, Upton R, Foster DJ, Christrup LL, Kristensen K,Hansen SH, Arendt-Nielsen L, Drewes AM. Pharmacokinetic-pharmacodynamic modeling of morphine and oxycodone concentrationsand analgesic effect in a multimodal experimental pain model. J ClinPharmacol. 2008 May;48(5):619-31.
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Dual E-R Model Schematic
Gut
StudyDrug dose
Central Peripheral
Effect
PD
Effe
ct
Gut
RescueDrug dose
Central
Effect
PD
Effe
ct
Total Observed PD Response
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PKPD in Proof of Concept Trials: IMMPACT 2008
Individual Contributions to Total Response
- Integrated model ofStudy Drug and RescueE-R
- Allows interpretation ofindividual and jointeffects
- Success of thisapproach highlydependent on adequateDose-Ranging design
- Results preliminary:Evaluation ofperformance throughsimulation ongoing
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PKPD in Proof of Concept Trials: IMMPACT 2008
Model-Based Inferences in the Presence ofDropout
- Acute pain PoC study
- Dropout after first rescue
- Population nonlinear-mixed effects exposure-response modeldeveloped from observed repeated-measures data (missing Atrandom assumption)
Approach first described in:
Sheiner LB. A new approach to the analysis of analgesic drug trials,illustrated with bromfenac data. Clin Pharmacol Ther. 1994Sep;56(3):309-22.
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PKPD in Proof of Concept Trials: IMMPACT 2008
Observed Data
0.25 1 2 4 6 8 10 12
Observed Pain Intensity Placebo (n=34)
Time (hr)
NP
RS
Score
Fre
quency (
%)
020
4060
80100
Rescued
NPRS = 10NPRS = 9
NPRS = 8NPRS = 7
NPRS = 6
NPRS = 5
NPRS = 4NPRS = 3
NPRS = 2NPRS = 1
NPRS = 0
0.25 1 2 4 6 8 10 12
Predicted Pain Intensity Placebo (n=34)
Time (hr)
NP
RS
Score
Fre
quency (
%)
020
4060
80100
0.25 1 2 4 6 8 10 12
Extrapolated Pain Intensity Placebo (n=34)
Time (hr)
NP
RS
Score
Fre
quency (
%)
020
4060
80100
0.25 1 2 4 6 8 10 12
Observed Pain Intensity 60 mg Patients (n=65)
Time (hr)
NP
RS
Score
Fre
quency (
%)
020
4060
80100
Rescued
NPRS = 10NPRS = 9
NPRS = 8NPRS = 7
NPRS = 6
NPRS = 5
NPRS = 4NPRS = 3
NPRS = 2NPRS = 1
NPRS = 0
0.25 1 2 4 6 8 10 12
Predicted Pain Intensity 60 mg Patients (n=65)
Time (hr)
NP
RS
Score
Fre
quency (
%)
020
4060
80100
0.25 1 2 4 6 8 10 12
Extrapolated Pain Intensity 60 mg Patients (n=65)
Time (hr)
NP
RS
Score
Fre
quency (
%)
020
4060
80100
0.25 1 2 4 6 8 10 12
Observed Pain Intensity 120 mg Patients (n=66)
Time (hr)
NP
RS
Score
Fre
quency (
%)
020
4060
80100
Rescued
NPRS = 10NPRS = 9
NPRS = 8NPRS = 7
NPRS = 6
NPRS = 5
NPRS = 4NPRS = 3
NPRS = 2NPRS = 1
NPRS = 0
0.25 1 2 4 6 8 10 12
Predicted Pain Intensity 120 mg Patients (n=66)
Time (hr)
NP
RS
Score
Fre
quency (
%)
020
4060
80100
0.25 1 2 4 6 8 10 12
Extrapolated Pain Intensity 120 mg Patients (n=66)
Time (hr)
NP
RS
Score
Fre
quency (
%)
020
4060
80100
0.25 1 2 4 6 8 10 12
Observed Pain Intensity 120 mg Patients (n=66)
Time (hr)
NP
RS
Score
Fre
quency (
%)
020
4060
80100
Rescued
NPRS = 10NPRS = 9
NPRS = 8NPRS = 7
NPRS = 6
NPRS = 5
NPRS = 4NPRS = 3
NPRS = 2NPRS = 1
NPRS = 0
0.25 1 2 4 6 8 10 12
Predicted Pain Intensity 120 mg Patients (n=66)
Time (hr)
NP
RS
Score
Fre
quency (
%)
020
4060
80100
0.25 1 2 4 6 8 10 12
Extrapolated Pain Intensity 120 mg Patients (n=66)
Time (hr)
NP
RS
Score
Fre
quency (
%)
020
4060
80100
Placebo
Dose 1 Dose 2
Freq
uenc
y of
NP
RS
Sco
re
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PKPD in Proof of Concept Trials: IMMPACT 2008
PD Response Time-Course
0 2 4 6 8 10 12
02
46
810
PI121 ID=2036
Time (hr)
Pain
Inte
nsity
0 2 4 6 8 10 12
02
46
810
PI121 ID=2037
Time (hr)
Pain
Inte
nsity
0 2 4 6 8 10 12
02
46
810
PI121 ID=2038
Time (hr)
Pain
Inte
nsity
0 2 4 6 8 10 12
02
46
810
PI121 ID=2039
Time (hr)
Pain
Inte
nsity
0 2 4 6 8 10 12
02
46
810
PI121 ID=2040
Time (hr)
Pain
Inte
nsity
0 2 4 6 8 10 12
02
46
810
PI121 ID=2041
Time (hr)
Pain
Inte
nsity
0 2 4 6 8 10 12
02
46
810
PI121 ID=2042
Time (hr)
Pain
Inte
nsity
0 2 4 6 8 10 12
02
46
810
PI121 ID=2043
Time (hr)
Pain
Inte
nsity
0 2 4 6 8 10 12
02
46
810
PI121 ID=2044
Time (hr)
Pain
Inte
nsity
Pai
n In
tens
ity (N
PR
S)
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Model-Based Extrapolation
0.25 1 2 4 6 8 10 12
Predicted Pain Intensity 60 mg Patients (n=65)
Time (hr)
NP
RS
Score
Fre
quency (
%)
020
60100
0.25 1 2 4 6 8 10 12
Predicted Pain Intensity 120 mg Patients (n=66)
Time (hr)
NP
RS
Score
Fre
quency (
%)
020
60100
0.25 1 2 4 6 8 10 12
Extrapolated Pain Intensity 60 mg Patients (n=65)
Time (hr)
NP
RS
Score
Fre
quency (
%)
020
60100
0.25 1 2 4 6 8 10 12
Extrapolated Pain Intensity 120 mg Patients (n=66)
Time (hr)
NP
RS
Score
Fre
quency (
%)
020
60100
Rescued
NPRS = 10
NPRS = 9
NPRS = 8
NPRS = 7NPRS = 6
NPRS = 5
NPRS = 4
NPRS = 3
NPRS = 2
NPRS = 1NPRS = 0
0.25 1 2 4 6 8 10 12
Predicted Pain Intensity 60 mg Patients (n=65)
Time (hr)
NP
RS
Score
Fre
quency (
%)
020
60100
0.25 1 2 4 6 8 10 12
Predicted Pain Intensity 120 mg Patients (n=66)
Time (hr)
NP
RS
Score
Fre
quency (
%)
020
60100
0.25 1 2 4 6 8 10 12
Extrapolated Pain Intensity 60 mg Patients (n=65)
Time (hr)
NP
RS
Score
Fre
quency (
%)
020
60100
0.25 1 2 4 6 8 10 12
Extrapolated Pain Intensity 120 mg Patients (n=66)
Time (hr)
NP
RS
Score
Fre
quency (
%)
020
60100
Rescued
NPRS = 10
NPRS = 9
NPRS = 8
NPRS = 7NPRS = 6
NPRS = 5
NPRS = 4
NPRS = 3
NPRS = 2
NPRS = 1NPRS = 0
Dose 1 Dose 2
- Repeated-measures nonlinear mixed effects model usedto extrapolate individual responses over time
- View simulated response time-course in the absence ofdropout
Freq
uenc
y of
NP
RS
Sco
re
Freq
uenc
y of
NP
RS
Sco
re
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PKPD in Proof of Concept Trials: IMMPACT 2008
Summary (1)The utility of exposure-response in PoC trials depends onstudy design and PoC goals.
- MTD-Type PoC E-R modeling of PoC data has minimal value; may be misleading
PK modeling still useful for understanding target population PK,reducing variability, or for explaining extreme outcomes
- Dose-Ranging PoC E-R has high value for design, analysis and PoC determination
Comparative E-R relationships across multiple endpoints/activecontrols provides insight into probability of achieving target productprofile
Advances knowledge building for future drug development phases
Basis for trial simulations to explore future designs
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PKPD in Proof of Concept Trials: IMMPACT 2008
Summary (2)
- PK and E-R modeling and simulation: are tools for knowledge-building and decision support in drug
development
provide basis for trial simulations to explore and optimize trialdesign performance
are best supported by trial designs that explore individual E-Rrelationships
of multiple endpoints allows quantitative assessment of drug’smultivariate response profile, supporting dose-selection decisions
may be useful in assessing test treatment response in presence ofrescue dosing (preliminary)
may be useful for making inferences in the presence of dropout(for non-regulatory purposes)
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PKPD in Proof of Concept Trials: IMMPACT 2008
Additional References- Krall RL, KH Engleman, HC Ko, and CC Peck. “Clinical Trial Modeling and Simulation – Warner KE, Peck
CC, Work in Progress.” /Drug Info J/, 32: 971-976, 1998.- Peck CC. “Drug development: Improving the process.” /Food and Drug Law J/. 52 (2):163-167, 1997.- Reigner BG, PEO Williams, IH Patel, J-L Steimer, CC Peck, and P van Brummelen. “An evaluation of the
integration of pharmacokinetic and pharmacodynamic principles in clinical drug development.” /ClinPharmacokinet/, 33(2): 142-52, 1997.
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